THE EFFECT OF ENDING CONSCRIPTION ON EMPLOYMENT
OPPORTUNITIES: EVIDENCE FROM ITALY
Massimo Franco Marini
B.A., California State University, Fullerton, 2007
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2011
© 2011
Massimo Franco Marini
ALL RIGHTS RESERVED
ii
THE EFFECT OF ENDING CONSCRIPTION ON EMPLOYMENT
OPPORTUNITIES: EVIDENCE FROM ITALY
A Thesis
by
Massimo Franco Marini
Approved by:
________________________________________, Committee Chair
Suzanne O’Keefe, Ph.D.
________________________________________, Second Reader
Kace Chalmers, Ph.D.
__________________________________
Date
iii
Student: Massimo Franco Marini
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for this thesis.
_______________________________, Graduate Coordinator
Jonathan Kaplan, Ph.D.
Date
Department of Economics
iv
Abstract
of
THE EFFECT OF ENDING CONSCRIPTION ON EMPLOYMENT
OPPORTUNITIES: EVIDENCE FROM ITALY
by
Massimo Franco Marini
Conscription has been long practiced in a number of European countries but has recently
been in decline. This paper attempts to identify how the probability of being employed
changes with the end of conscription. Data was provided by the Banca D’Italia – Survey
of Household Income and Wealth – and analyzed for differences in employment
opportunities following the end of conscription in 2005. A logit model was constructed
and a triple-difference estimation method was utilized to examine employment
differences between skilled and unskilled labor, less than 25 years, in 2002 and 2006.
The triple-difference estimator found no significant changes in employment opportunities
caused from the end of conscription. Further analysis of older cohorts revealed
heterogeneity, as unskilled cohorts between 31 and 40 years experienced better
employment opportunities after conscription.
________________________________________, Committee Chair
Suzanne O’Keefe, Ph.D.
Date
v
To men of the capitalist world,
May a benevolent path guide us
to masculinity through creation,
away from slaughter
vi
ACKNOWLEDGEMENTS
Special thanks to Professor O’Keefe for heading my committee and for her insights and
suggestions that strengthened the theoretical and empirical models.
I owe my deepest gratitude to my second reader, Professor Chalmers,
whose use of language and eye for detail ultimately transformed
this former school project into a proper thesis.
And to Allyson Crosby, whose fluffy writing style reminds me
people and cultures are more than just statistics – thank you.
vii
TABLE OF CONTENTS
Page
Dedication .......................................................................................................................... vi
Acknowledgements ........................................................................................................... vii
List of Tables .......................................................................................................................x
List of Figures .................................................................................................................... xi
Chapter
1. INTRODUCTION .........................................................................................................1
2. LITERATURE REVIEW ..............................................................................................8
Labor Market Organization in Italy......................................................................14
Conscription Reform of 2005 ...............................................................................20
3. ECONOMIC FRAMEWORK .....................................................................................22
Empirical Regression Strategy .............................................................................25
Descriptive Statistics ............................................................................................29
Regression Strategy and Sign Predictions ............................................................31
4. REGRESSION ANALYSIS ........................................................................................34
Heterogeneous Data Concerns .............................................................................42
5. STUDY LIMITATIONS AND CONCLUSION .........................................................50
viii
End Notes ...........................................................................................................................54
Appendix A: Calculations of the Theoretical Model .........................................................56
Appendix B: Further Regressions to Check for Heterogeneity .........................................58
Works Cited .......................................................................................................................61
ix
LIST OF TABLES
Page
Table 1: Descriptive Statistics of Sample from 2002 and 2006, Individual Years ............30
Table 2: Regression Results, Individual Years ..................................................................35
Table 3: Regression Results, Pooled Data .........................................................................38
Table 4: Descriptive Statistics, Cohorts Aged Between 25-30 Years, Individual Years. ..44
Table 5: Descriptive Statistics, Cohorts Aged Between 31-40 Years, Individual Years ...46
Table 6: Regression Results, Pooled Cohorts ....................................................................48
x
LIST OF FIGURES
Page
Figure 1: Labor Market with Minimum Wage Floor and Labor Supply Shock ................24
xi
1
“L’Italia é una Repubblica democratica, fondata sul lavoro.”
Italy is a democratic Republic, founded upon labor.
First sentence of Article 1, Constitution of the Italian Republic
Adopted December 22, 1947
Chapter 1
INTRODUCTION
Conscription, or forced military service, is a concept foreign to common law countries
like the United Stated and Great Britain, but is known well to many generations of men
from most European nations, as well as their former colonies. The first widespread use
of conscription was seen during the reign of Napoleon in France in 1804 (Mulligan and
Shleifer, 2005). The practice was subsequently adopted by Prussia in 1813, only seven
years after their defeat by Napoleon’s army (Hagemann, 2007). Since then, conscription
has remained in practice in many European nations until France and the Netherlands
began to abolish the system in 1997. Spain, Sweden, Italy, and Germany, along with
several other nations, have followed suit, all abolishing conscription within the last
decade.
Conscription regulation can vary widely between countries throughout history. In
Italy in 1861, the unification of warring city-states after hundreds of years was indeed a
cause for celebration. But the young nation had to find ways to protect itself from the
2
much stronger French and Austrian Empires. In 1865, the new Italian government
required the conscription of all males 18 years or older (Cole, 1995). This concept was
not entirely revolutionary in Italy, as conscription had been required in the VeniceVeneto region since 1792. Understandably, this measure was unpopular in the agrarian
South, being seen as an unfair request from the ruling North (a political and economic
fissure that continues to present day). The loss of seasonal farmhands was detrimental to
the economy and protests ensued. But after several years the practice was accepted and
all men born after 1854 had to report for medical examinations, whether they were
disabled or ineligible, at which time the regional military districts would decide their
future.
Most commonly scrutinized as a violation of basic liberties, conscription has
conversely been seen as a rite of passage for men. Prussia required all men 17-40 to be
conscripted during times of war and later during times of peace. The government
promised more political rights to those who willfully served (Hagemann, 2007). This
proclamation was directed at landless citizens from Prussia, who enjoyed few, if any,
political rights, such as suffrage. The Prussian government instituted conscription as a
patriotic-national mobilization – the creation of the “valorous Volk family,” or “glorious
people’s family” (Hagemann, 2007). Men of all classes were to be united under a
national cause driven by internal national action.
From almost the inception of conscription came a redefining of citizenship, and
with it, a new definition of masculinity. In ancient and non-capitalist cultures, rites of
3
passage were tied to religious ceremonies like Bar Mitzvahs in the Jewish faith. In
Africa, boys were taken on hunting trips with the men of the tribe, or were sent out alone,
to return a man with the full benefits of manhood bestowed upon them, including the
right to marry. The belief in conscription has tied manhood to the training for defense of
a nation, killing other humans if called to battle, a concept that is theoretically at odds
with “human development”. Some have argued convincingly that conscription’s most
significant contribution has been symbolic – shaping and communicating a national
vision – rather than functional – raising an army (Selmeski, 2007).
In Europe, manhood and masculinity were appropriated and redefined by the
government. “Military enthusiasts believe and try to make us [men] believe, war is the
only stimulus known for awakening the higher ranges of men’s spiritual energy” (James,
1906). While providing work and experience for poor men, conscription also required
formally protected bourgeoisie men, who were shielded by their wealth from serving, to
serve1. Post-Napoleon France went further, requiring women, children, and the elderly to
serve the state during times of war, in a manner similar to war rationing but through work
(Forrest, 2007), though this practice was not widely adopted elsewhere. As a result,
women’s civic power was subsequently stifled as their citizenship and rights in a society
could never be fully realized.
Italy required conscription for all men between 18 and 27 years for a period of
one year (CIA, 2009). Conscription in present-day Italy has changed very little. Until
1999, men were not only required for the military, but were the only sex allowed to serve.
4
With the passing of Law n.380/1999, women were finally allowed to join any branch of
the Armed Forces, though they were only admitted to logistical positions. As of 2002,
women had a negligible impact, only accounting for .1% of the nearly 500,000 person
total military (NATO, 2002). In 2000 and 2001, the number of conscripts required by
Italy was close to 270,000 men each year. However, with the announcement in 2000 that
conscription would be ending in 2007, Italy reduced the number of conscripts required
each year beginning in 2002 (Piattelli, 2001). In 2002, the government quota was set for
59,400 men with an average reduction of 11,880 men each year until 2007. Since new
recruits were in higher supply than predicted, Italy was able to end conscription two years
early and today is fully defended by professional soldiers.
Economically, there has been a long standing belief that conscription has a
negative impact on human capital growth and restricts basic labor liberties, namely the
right to choose where one works based on their skills and desire (Friedman, 1967).
Conscription has been the topic of hot debate in the United States since the Vietnam War.
This was the last time the government “confiscated labor” (Williams, 2004), and it has
been proposed several times at the start of major wars. Representative Charles Rangel
proposed the Universal National Service Act in 2003, which would have required the
military draft to resume recruitment of eligible young men and women. Though it was
soundly defeated 402-2 (USS, 2003), and has been continuously defeated in subsequent
years, conscription is still common place for over forty countries.
5
But since 1997, Croatia, Albania, and Ukraine, as well as others previously
mentioned, have changed from conscription to an all-volunteer force (AVF). Other
countries have also reduced their mandatory service lengths and many recognize
alternative service as a viable option to military service. This raises some important
questions about the nature of conscription in terms of employment opportunities.
Without conscription, men will be able to plan their lives differently, such as entering a
university or starting a family earlier. Young men could spend their formative years in
higher education, and therefore countries that end conscription should have a better
educated workforce and also potentially a larger supply of unskilled labor. Some of these
relationships can now be empirically analyzed, given data from Italy.
Several authors have attempted to characterize why conscription still exists today
and what has led to its decline. Jehn and Selden (2002) believe the move away from
conscription is due to two simultaneous causes. The first is membership of recently nonconscripted countries in NATO, and the second was the fall of the Soviet Union. One
would think that two world powers in close proximity would be likely to have standing
armies at all times. Indeed the Soviet Union had conscription from the beginning of its
inception, a point deeply defended by Soviet founder Leon Trotsky (1940). Yet it does
make intuitive sense that even if one power fell, the other power would still keep their
conscripted army for several years after, until the threat appeared to be neutralized. This
might explain the move away from conscription not beginning until 1997 instead of
shortly after the Soviet Union fell in 1991.
6
Though these theories seem plausible, only Mulligan and Shleifer (2005) have
any empirical evidence, while most other arguments are of a moral and political nature.
A majority of the literature on conscription is theoretical, with few empirical experiments
conducted. This paper hopes to respond to this void of empirical research about
conscription. While one study that examined changes in wages during and after
conscription (Imbens and Van Der Klaauw, 1995), there has not been a study specifically
analyzing the differences in employment opportunities.
Since conscription is a policy change that affects mostly men of a country
(notable exception include Israel and North Korea; CIA, 2009), women appear to be the
control group. However, when unconscripted men enter the labor market, employment
opportunities for both men and women are similarly impacted. Unconscripted men will
be treated as unskilled labor, in competition with unskilled women and immigrants for
positions. Employment opportunities between skilled and unskilled labor are analyzed to
determine effects from ending conscription. A triple-difference estimation method is
used to examine the difference, built within a logit specification. Assuming no other
shocks have occurred in the labor market, the effect identified by the triple-difference
estimator is believed to isolate the effect of ending conscription. Certain individual
characteristics which are believed to have an effect on employment, such as education,
region of residence, and sector of employment are included. The data are pooled to
calculate the effects of ending conscription on employment opportunities for the cohort
aged 18-24 years. Given wage rigidity and union power in Italy, the theoretical model
7
shows employment prospects for unskilled labor will decrease after conscription, at least
in the short-run when treating conscription as a temporary positive unskilled labor supply
shock.
Results show that there is an unexpected 9.5% increase in the probability of
employment in 2006 compared to 2002 for unskilled labor, relative to skilled. This
effect, however, is found to be statistically insignificant. I suspect heterogeneity by age
may lead to this insignificant result. To test for heterogeneity among individual years,
the conscription interaction term is separated by year of age, yet coefficients are found to
be statistically insignificant for all ages. Heterogeneity across cohorts was also
considered, and further analysis shows the effect on the older age groups with mixed
significance in Section 4.2. Other characteristic variables are found to be significant
when determining employment, expected based on other human capital accumulation
models (Becker, 1994).
This paper follows with a literature review of the current empirical and theoretical
work on conscription, and an outline of the Italian labor market in Section 2. Section 3
will discusses the economic framework, theoretical model, data, and model
specifications. Section 4 discusses the regression results, revisits the theoretical
assumptions, and discusses tests for heterogeneity. In Section 5, I discuss the reality of
the assumptions, limitations of the study, future research possibilities, and conclude.
8
Chapter 2
LITERATURE REVIEW
The literature review will be comprised of two primary areas of interest. First, there are
studies that directly examine the effects of conscription, which are not specific to Italy.
These papers mostly explain how conscription affects educational attainment and the
moral implications it arouses. The second area of interest pertains to studies conducted
about the Italian labor market, but with no specific focus on post-conscription labor
market opportunities. Each area is examined in depth individually before identifying the
common elements in an effort to determine the ideal economic framework.
Partially described in Section 1, a large amount of the literature on conscription
comes from the ideological battles that consistently arise when the subject is mentioned.
These politically-charged arguments are great in quantity but have evolved little since the
1960s when a large majority of the studies and papers were written. During the Vietnam
War, cutting-edge economists such as Walter Oi and Milton Friedman were on the
forefront of the arguments against conscription. Both were conservative economists as
the country was shifting away from Keynesian economics; their pro-liberalization
theories coupled with massive anti-draft public sentiment made conscription an easy
target.
Friedman (1967) argued that conscription would require an even larger military
structure, and specifically an unacceptable increase in government bureaucracy. Under
conscription, arbitrary power would be given to draft boards to direct the outcome of the
9
most important years of a young man’s life. This is similar to the power military districts
wielded in Italy. Conscripts would live in squalor and receive wages far less than their
market equivalent because the government could operate as a monopsony and therefore
had no motivation provide better living conditions or wages. In one form or another, this
is the argument that most economists use against conscription (Mitchell, 1999; Oi, 2003;
Rose, 2002). In terms of lost lifetime wages there hardly seems anything to argue. If
conscripts earn half their market entry wage in the military for a year, they will obviously
be worse off than non-conscripts. A theoretical extension of this concept is described by
Poutvaara and Wagner (2007) when they look at the dynamic costs of conscription on a
society. They find that not only are conscripts worse off in utility, but they are actually
hurt twice if conscription ends. First, they are paid insufficient wages as conscripts and
later, if conscription ends, they are required to pay higher taxes to afford an all-volunteer
force.
Conscription can also be seen as an in-kind tax on society, where labor and
resources are the payment for national security. Reeves and Hess (1970) address this and
several other concerns with conscription, such as more wars being started given an
endless military supply, the decline in real output, and higher overall costs. Friedman
(1967) defeated the first argument, saying there were no studies that proved this theory.
He then describes the decline in real output as men are taken out of the labor supply and
forced into less productive military professions. Civilian productivity is almost certainly
higher than government productivity, but he makes no assertions as to how many men
10
would have gone to college instead of the labor force. Had an increase in college
attendance been noted, overall lifetime wages have a strong possibility of eclipsing wages
earned from the unskilled labor opportunities Reeves and Hess argued.
The third argument assumes soldiers who want to be soldiers will be better than
drafted citizens, so their turnover will be lower. Oi (2003) agrees, expanding that a
higher reliance on capital and technology has resulted in fewer deaths and higher wages.
Though the economic arguments against conscription are compelling on their own, the
political arguments are clearly the heated topics of debate. Reeves and Hess (1970)
close, stating “the nation-state system itself demands that the state be accorded absolute
loyalty…that process is inimical to freedom.” Their concluding remarks sum the
political argument against conscription, from Adam Smith (Rose, 2002) to modern day
politicians.
Though not nearly as numerous, there are some arguments that favor conscription
over the all-volunteer military. Mulligan and Shleifer (2005) offer a unique empirical
perspective as to the origins of why conscription was so popular but has recently started
to decline in practice. To countries like the United States and the United Kingdom,
conscription is viewed as practiced by Communists and totalitarian governments. Their
suspicions are correct for a number of reasons. First, common law countries like the
United States and the United Kingdom lack the bureaucratic infrastructure created by
countries that use a French-inspired legal system, such as most of Europe. They argued
that countries like Napoleonic France or Prussia used conscription because the costs
11
associated with it were reduced by the sheer size of the government and economies of
scale. Ng (2008) built a theoretical model showing this to be the primary cause for a
country to choose conscription over volunteers. Conscription may actually be welfare
improving if the country requires a large standing force, such as the historical examples
above and China and North Korea today.
However, the previous arguments do not necessarily support conscription as much
as they support maintaining the status quo. Mulligan and Shleifer (2005) conclude that
conscription is a measure of regulatory ability. It is simply less expensive to continue the
current system rather than attempt to inact a reform. At the same time, conscription can
be seen as regulatory inability, as Lokshin and Yemtsov (2008) find in Russia. In their
study, not only does conscription affect men of lower socioeconomic backgrounds
disproportionally when compared to their richer cohorts [in concurrence with Maurin &
Xenogiani (2005)], but Russia would be unable to shift to an all-volunteer force given
their level of corruption. Marini (2010) found Russia was the country least likely to end
conscription in a sample of 24 European countries. Its predicted and publicly stated end
date will not be until at least the 2030s (“Russia To Keep,” 2008).
Research on the effects of conscription on educational attainment and wages are
the most common empirical studies undertaken, given the natural experiment changes in
conscription create. Maurin & Xenogiani (2005) found that conscription in France did
not directly affect entry wages, but affected overall lifetime wages only in terms of lost
education. An 18 year old non-conscript and a 19 year old conscript fresh out of the
12
military will make the same wage, as the authors follow find that one year of training in
the military is worthless in the workplace2. This seems plausible given that conscription
has been in place so long that most employers would be expecting new unskilled labor to
have at least one year of military experience. In fact, they find that the foregone year of
university education due to conscription is causally responsible for a 13% decrease in
later wages. Imbens and Van Der Klaauw (1995) found similar results using data from
the Netherlands but with less dramatic results. Conscription there is responsible for about
5% overall lower wages ten years after the conscript exited the military.
The remainder of the literature review examines the state of the Italian economy
and how the labor market might be affected by a positive labor supply shock. Balmaseda
et al. (2000) constructed a vector autoregressive model with shocks to real wages, output,
and unemployment using data from OECD countries between 1950 through 1996. With
respect to conscription and wages, they found that Italy had higher wage rigidity than
other countries examined. This leads to the belief that wage stickiness is not only present
in Italy, but it is stronger (less susceptible to change) than other comparable countries in
Europe. Though Italy may have average wage growth, their job separation rate is the
lowest in Europe (Hobijn and Sahin, 2007), indicating a highly regulated labor market
(explained in greater detail in Section 2.2).
Mirilovic (2007) extends this idea in a paper similar to Mulligan and Shleifer
(2005) when he investigates what characteristics influence conscription today. He finds
countries with highly regulated labor markets are more likely to choose conscription.
13
Stated another way, given the sizeable bureaucracy in Italy, conscription and labor
market regulations can flourish together. Labor regulations are a complex web of
different laws, of which conscription is one component. Since conscription reform
changes overnight, it is understandable that conscription can seem like a cause of
bureaucracy to economists like Friedman and Oi when in fact it is simply a result of it.
Mirilovic’s argument essentially states that the labor markets affect conscription, not the
other way. This paper will assume the latter in the long-run3.
Mirilovic is correct, however, in believing that the actions of the labor market
impact men during conscription. Italy has higher unionization, youth unemployment (1524 years), and long-term unemployment when compared to the OEDC average (OECD,
2010). Mirilovic shows these labor market distortions make it harder for former
conscripts to find jobs. When including declining labor force participation over the past
decade, the highest percent of discouraged workers under 34 years in Europe, and low
fertility rates, it reveals a relatively bleak picture of the Italian labor market. But what
impact would these conditions have on newly unconscripted men?
Given the grim job prospects for youth they may instead decide to stay in the
military and forego education. This is particularly true for conscripts from disadvantaged
backgrounds as noted previously (Lokshin and Yemtsov, 2008). Since the minimum
conscription time affords few, if any, training benefits, the government needs to find a
better way to provide some job training to the population least likely to formally pursue
higher education. Maurin & Xenogiani (2005) also show that wages were kept higher
14
during conscription. After it ended, there was a significant decrease in the demand for
education, which implies an increase in the unskilled labor pool and reduction real wages
for young men. Similar results are found in Section 3 below.
Ballarino and Bratti (2009) examined the Italian labor market when they studied
the effects of the students’ field of study on job opportunities4. They found that an
increase in short-term contracts, or temporary workers, which are restricted in use by
union rules, led to an overall negative employment outlook. Young workers are often
underemployed even as they acquire a range of skills in their quest for permanent
employment. This has led to countercyclical patterns between too many graduates and
not enough jobs, with an overall worsening job market for college graduates. At this
point, it is appropriate to give an outline of the functionality of unions, collective
bargaining agreements, and age dispersion in the labor market in Italy so that the reader
can fully understand their respective and collective impacts.
Labor Market Organization in Italy
The Italian labor market is characterized by several features not seen in many
other OECD countries. First, Italy is one of only four countries in Europe not governed
by a federal minimum wage mandate (FedEE, 2010). In the classical sense, this would be
the perfect labor market, as wages could adjust as needed without interference from
government distortions. The second feature is the great extent under which workers are
15
covered by the network of collective bargaining agreements and a highly regulated labor
environment.
A last feature, unrelated to wages but influential in the labor market composite, is
Italy’s distinction of “world’s oldest country”; that is, it has both the highest proportion
of the population over 65 years and lowest proportion less than 15 years (Mosca, 2009).
Of OECD countries, only Japan has a similar age distribution. Since the analysis will
focus on men, unskilled and under 25, it is important to address these issues before
attempting to build a theoretical framework in Section 3.
The first two features need to be explained together, since the second is the reason
the first does not exist. When Italy adopted their new constitution in 1947, there was a
distinct anti-government feeling following 20 years of Mussolini’s Fascism. Because of
this, people demanded to have more control over their labor (see quote from the Italian
Constitution above). These anti-government sentiments, combined with notoriously high
Communist participation, gave trade unions immense power to control wages, strikes,
and conditions for termination. Unions represent over 80% of workers in the labor force
today (Peng and Seibert, 2008), just eclipsing 400 sectors represented. They are
separated into various collectives, from in-company, to sectional, national levels. The
options available to new employees are quite staggering when compared to the United
States labor market. At the workplace level, employee councils are made up of delegates
elected from each shop or office. These councils represent all employees in the shop,
whether or not all are members of the union. In keeping with rights of labor, employees
16
have the option of joining the union, but if they pass, they still have the legal right to vote
for delegates who will ultimately influence their lives at work (Barkan, 1984).
There are two branches of unions; categorical unions and geographical unions.
Categorical unions are industry specific, representing employees such as hotel workers,
metalworkers, and textile employees. Geographical unions operate as the name implies,
creating relationships and agreements between workers of different industries within a set
geographical region. In keeping with the right to choose one’s labor association, there
are even further splits based on ideological differences, such as Fascist, Socialist, and
Communist unions, all of which may be present within one firm. However, the largest
unions today are the General Italian Confederation of Labor (CGIL), the Italian
Confederation of Workers’ Unions (CISL), and the Italian Union of Labor (UIL), all of
which grew out of political parties but are now fully autonomous, professional
organizations (BEEA, 2010). These characteristics are important to note when
comparisons are made between countries. Both the closed-shop arrangements (everyone
must join a union) and the U.S. union-shop (nonmembers must pay initiation fees and
dues) seem highly undemocratic to many Italians (Barkan, 1984).
Unions in Italy are responsible for renegotiating wages though collective
bargaining agreements every three years. The new collective bargaining agreement went
into effect January 2010. This new agreement was largely a reformation of the 1993
agreements that reduced wages when faced with increased unemployment during the
Italian economic crisis of 1991-93 (Contini et al., 2008). The power of the unions lies
17
with their ability to increase wages regardless of the firms’ profits. This power implies
there are two economic phenomena at work here. First, we can observe that wages
exhibit a “ratchet effect” as explained by J. M. Keynes (1936/2006) in the following
passage:
The fact that wages tend to be sticky in terms of money, the money-wage being more stable than
the real wage, tends to limit the readiness of the wage-unit to fall in terms of money. Moreover, if
this were not so, the position might be worse rather than better; because, if money-wages were to
fall easily, this might often tend to create an expectation of a further fall with unfavourable
reactions on the marginal efficiency of capital. Furthermore, if wages were to be fixed in terms of
some other commodity, eg. wheat, it is improbable that they would continue to be sticky. It is
because of money’s other characteristics — those, especially, which make it liquid — that wages,
when fixed in terms of it, tend to be sticky.
Summarizing, he says wages are sticky and flexible upwards but inflexible downwards;
they tend to “ratchet-up” without the likelihood of falling. With such incredible worker
bargaining power, unions can almost guarantee that wages never fall (the mandates in
1991 and 1993 that decreased wages were unprecedented) as a result of economic
conditions.
Italy has the strictest labor market when it comes to collective dismissals and
ranks tenth overall in European employment protection (OECD, 2010). Given union
power, it is safe to assume that this wage stickiness is time-dependent – wages are
exogenous and would be evaluated after economic conditions have been evaluated –
rather than state-dependent – endogenous of market changes, such as a labor shock. In
this case, wage stickiness is caused by unions, who try to get higher wages regardless of
market conditions, and therefore negotiate collective bargaining agreements regardless of
18
an employer’s forecast or prevailing economic conditions. This is a reasonable
assumption given union power in Italy.
The last characteristic to be addressed is that of the age distribution of the Italian
labor force. If non-conscripted men are to forego the military or college, it is important
to identify the current opportunities available to them in terms of their age demographic.
Between 1988 and 1998, small- and medium-sized enterprises (SMEs) shifted their
primary entry-level workforce from employees aged 20-24 years to 25-29 years (Contini
et al, 2008). The authors also noted that this distribution continues to present day, a
result of younger employees “experimenting” with different job solutions and lack of
experience in any one sector. This causes job matching conditions to be less optimal for
businesses, at particularly higher costs for SMEs, causing a shift to the next oldest cohort
(Leombruni and Quaranta, 2002). This means that job opportunities for cohorts of
unconscripted males were decreasing several years before they entered the labor force.
As the age of the population of Italy increases, employers are going to need to
hire more permanent positions from younger labor pools. There should be an increase in
positions available to unskilled young men, aged 15-24. However, Italy has been
experiencing a decrease in fertility rates for over twenty years and perhaps the labor pool
of men 15-24 years is too small and unskilled when compared to men in the next oldest
cohort (Contini et al., 2008).
Mosca (2009) further examined the effects of low fertility rates on the Italian
labor market, finding lower employment rates among members of larger generations in
19
both skilled and unskilled work. This may seem to be good news for members of the
current youth population, which would be considered historically a small generation.
However, for both skilled and unskilled labor, employment opportunities become positive
at the same age. This means that smaller cohorts have better job opportunities than larger
cohorts, but no ground can be made up in the current labor market. Presently, an
abnormally small generation is following the exit of an abnormally large generation as
they enter and exit the market at the same rate. As such, the labor force is predicted to
decrease in size and age over the next 45 years. Adding to this problem, Italy has the
lowest job-finding rate in continental Western Europe (2.58% annually), which is 4 times
lower than Europe and 20 times lower than the United States (Hobijn and Sahin, 2007).
This means that unemployed persons have a very low probability of finding suitable
employment.
It is appropriate at this point to explain two further characteristics that affect the
labor markets in Italy – those of educational attainment and geographical regions. Italy’s
economic roots lie in manual labor and heavy industrialization, which today are cause for
Italy to be a top ten world economy in terms of GDP size. But unfortunately, a manual
labor driven economy does not necessarily promote pursuit of higher education, as
several years of potential wages would be foregone. This has caused future generations
to be better educated than their parent, but at a rate below the OECD average (sixth from
bottom) in 2008 (OECD, 2010). As finance and telecommunications expand,
manufacturing has declined in developed countries. Italy’s status as a PIIGS (Portugal,
20
Italy, Ireland, Greece, and Spain) potential problem country could be influenced by their
passive response to technological shifts.
The second characteristic – regional differences – have specific and long-run
consequences in the country. The industrialized North has long enjoyed higher wages
and better job opportunities than the agrarian South, which includes the islands of Sicily
and Sardinia. Theories for this include better education, an urban economy, greater social
stability, and even a greater sense of civic pride. Nelson (2006) believes civic
commitments are lower in the South, creating breeding grounds for corruption and the
resurgence of the Mafia. These institutions are not business friendly and today contribute
to Southern Italy suffering from unemployment as high as 40% among working-age
people (“Internal Affairs,” 2011).
Conscription Reform of 2005
On July 29, 2004, the Italian Parliament adopted Law 4233-B, relating to the
“early suspension of compulsory military service and regulation of previously enlisted
voluntary servicemen” (WRI, 2008). The change had originally been announced in 2000
for a tentative end date of 2007, but enlistments were higher than expected. Conscription
in Italy could be seen as a holdover from imperial times. Less so than countries like
Ukraine or Poland, where ending conscription could be seen as breaking with
Communism, Italy may have realized that conscription is inefficient. Though
bureaucracy is likely the reason conscription continued to recent times, Italy’s labor force
21
may need young unskilled labor. In 1998, Italy conscripted about half of all 18 year old
males; an estimated 270,000 men. By 2002, that number dropped to one-fifth5.
Increased military pay may lead one to believe that conscription was instrumental in
keeping youth unemployment down. But youth unemployment rates are soaring, the
economy is stagnant, and the government has resorted to paying women €1000 to
conceive another child and paying women to not have abortions for economic reasons
(“Italian Region,” 2010). An act such as this implies that the government needs more
Italians, not more soldiers specifically. By ending conscription, young men could finish
college sooner and start families slightly earlier. Though it is outside the scope of this
paper, one could argue that low fertility rates were a leading cause for the reform.
In 2006, Italy experienced the greatest surge in GDP in the last decade. With
strong unions looking to increase membership and the increased use of short-term
contracts, SMEs specifically were able to absorb this influx of workers (Contini et al.,
2008) during a booming year. As stated previously, less educated men will have the
worst employment opportunities, though it is unnecessary to distinguish between lowskilled and high-skilled blue-collar workers in terms of employment opportunities, as the
difference is not statistically different from zero (“Thematic Feature,” 2005).
22
Chapter 3
ECONOMIC FRAMEWORK
Since conscription ended in Italy in 2005, young men previously required to join the
military will now presumably either enter the labor force or continue to enter college as
planned previously. In this case, because the military is now voluntary, it is required to
pay a desirable and competitive wage, requiring the military to operate in the same labor
market as private businesses. Eligible conscripts are not attending college at significantly
different rates before and after the reform (Di Pietro, 2009), so they must therefore enter
the labor force6. From a theoretical perspective, ending conscription can be seen as a
positive shock to the labor supply. In most countries, an increase in the labor supply will
increase unemployment due to minimum wages laws and sticky wages. Italy has no
federal minimum wage laws but is instead regulated by a vast network of collective
bargaining agreements.
To examine the Italian labor market for effects of conscription, it is first useful to
explain the labor supply shock in terms of unemployment. Adopted from Yashiv (2000),
a matching function, M, is created and treated like a production function in Equation 1
𝐻𝑡 = M(𝜇𝑡 , 𝐶𝑡 𝑈𝑡 , 𝑉𝑡 )
where
𝐻𝑡 : flow of new hires
𝜇𝑡 : level of job matching technology
(1)
23
𝐶𝑡 𝑈𝑡 : “efficiency units” of searching workers, a product of search intensity, 𝐶𝑡 ,
and unemployed workers, 𝑈𝑡
𝑉𝑡 : job vacancies opened by firms
So it can be seen that the matching function is described by the movements of job finding
technology, “efficiency units”, and vacancies available. In Equation 2, a partialequilibrium framework determines the impact of a labor supply shock (ending
conscription) on unemployment in the next period.
𝑈𝑡+1 − 𝑈𝑡 = −𝐻(𝜇𝑡 , 𝐶𝑡 𝑈𝑡 , 𝑉𝑡 ) + 𝑠𝑡+1 (𝐿𝑡 − 𝑈𝑡 ) + (𝐿𝑡+1 − 𝐿𝑡 )
where
𝑠𝑡+1 : separation rate
𝐿𝑡 : labor supply
𝑈𝑡+1 − 𝑈𝑡 = 𝛥𝑈: change in unemployment in period t+1
𝐿𝑡+1 − 𝐿𝑡 = 𝛥𝐿: change in labor supply in period t+1
𝐿𝑡 − 𝑈𝑡 : employment rate
With conscription ending this period, that would necessarily force labor supply next
period to be higher than the current period
𝐿𝑡+1 > 𝐿𝑡
Holding the matching function and separation rate constant, an increase in the labor
supply next period will cause unemployment to increase next period as well (see
Appendix A for full derivative and skilled labor market changes). Specifically, this
(2)
24
model predicts a unit change of one; that is, a one unit change in the labor supply next
period will cause a one unit change in unemployment, with both changes moving in the
same direction.
Admittedly a simple framework, it does show the predicted effect of conscription on the
employment rate. For further analysis of both short- and long-run effects, it is necessary
to examine the labor market supply and demand for changes in structural unemployment.
Figure 1 shows a basic labor market, with labor supply, ns, labor demand, nd, and a
minimum wage floor, WF. In this example, equilibrium wages should be lower, at point
A, but due to collective bargaining agreements, there is supply of unemployed workers
̅̅̅̅, caused from structural unemployment. When conscription ends, the labor
on line BC
supply curve shifts out (right) to ns’. Since wages are unable to fall, structural
̅̅̅̅.
unemployment has increased in the labor market to line segment BD
Figure 1: Labor Market with Minimum Wage Floor and Labor Supply Shock
25
Since wages do not change overnight (in contrast to conscription), employers are bound
in the short-run to previous wage contracts. Realistically, wages may still be falling in
some sectors but market wages are believed to be sticky in the short-run and exhibit the
“ratchet effect” explained previously7.
Empirical Regression Strategy
In an attempt to quantify the effects of conscription, it is necessary to make two
assumptions that will be discussed in detail. The first assumption requires that all
sampled men were eligible to serve in the military as conscripts; none of the individuals
were ineligible or disabled. The second assumption requires that no other supply shocks
affect the labor supply except for the unconscripted labor shock. In Italy, the most likely
form of a labor supply shock came in 2002 with the passing of the Bossi-Fini Law. This
immigration regularization amendment to the previous 1998 immigration statute allowed
immigrants who paid into the pension system and maintained continuous employment to
apply for “green-card” status (Levinson, 2005). As a result, the number of total legal
immigrants in Italy increased threefold to well over 630,000 in 2003, with thousands
more undocumented.
Using data supplied by the Banca D’Italia, the sample from 2006 will be used as
the treatment group despite the availability of the 2008 sample, which would include the
26
beginnings of the world financial crisis8. Data has been made available on the
employment status of the sample. Since employment is a dichotomous variable,
𝐸 ∗ = [𝐸𝑖 ] and 𝐸𝑖 ∈ [0,1] where 𝑖 = 1,2, … , 𝑇
where
𝐸 ∗ : true probability of employment
𝐸𝑖 : dichotomous behavior of choosing employment over unemployment
𝑇: sample size
It follows that in either year, employment opportunities are modeled in Equation 3
∗
𝐸𝑖,𝑡 = 1 𝑖𝑓 𝐸𝑖,𝑡
>0
(3)
𝐸𝑖,𝑡 = 0 𝑖𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Expanding this idea further, household characteristics believed to have an effect on
employment opportunities are presented in Equation 4
(β0 + β1 𝑋𝑖,𝑡 + β2 𝑀𝑖,𝑡 + β3 𝑇𝑖,𝑡 + β4 𝑀𝑖,𝑡 𝑇𝑖,𝑡 + ε𝑖,𝑡 )
(4)
These regressors are added to determine the probability of employment in Equation 5
∗
𝑃(𝐸𝑖,𝑡 = 1) = 𝑃(𝐸𝑖,𝑡
> 0) =
𝑃[(β0 + β1 𝑋𝑖,𝑡 + β2 𝑀𝑖,𝑡 + β3 𝑇𝑖,𝑡 + β4 𝑀𝑖,𝑡 𝑇𝑖,𝑡 + ε𝑖,𝑡 ) > 𝑢𝑖 ]
(5)
27
where
𝑢𝑖 : probability of unemployment
Finally, because the regressand is dichotomous, it will follow a cumulative density
function. The logit regression for individual years is presented in Equation 6
𝑃
ln ((1−𝑃)) = β0 + β1 𝑋𝑖,𝑡 + β2 𝑀𝑖,𝑡 + β3 𝑇𝑖,𝑡 + β4 𝑀𝑖,𝑡 𝑇𝑖,𝑡 + ε𝑖,𝑡
(6)
Here, Ei,t is a dummy variable indicating if the observed is employed, Xi,t is a set of
individual household characteristics believed to have an influence on employment
opportunities, Mi,t is a dummy variable indicating the subject is unskilled, and Ti,t
indicates if the observed is under 25 years of age. Following Di Pietro (2009) and
Maurin & Xenogiani (2005), a difference-in-difference (DiD) estimation method will be
used to measure the effects of conscription on the probability of employment between
skilled and unskilled labor, over and under 25 years of age. The regression will be
repeated with 2002 data to give two separate views of the labor market opportunities,
with results presented in Table 2. In regressions for both years, the DiD estimator is β4.
After identifying the different employment probabilities between young unskilled
labor in 2002 and 2006, it is necessary to determine if the difference is statistically
significant. A pooled data set is created by pooling the 2006 sample into the 2002 sample
28
and an interaction term is created to identify unskilled labor less than 25 years from the
2006 sample, Di,2006.
𝑃
ln ((1−𝑃)) = β0 + β1 𝑋𝑖,𝑡 + β2 𝑀𝑖,𝑡 + β3 𝑇𝑖,𝑡 + β4 𝐷𝑖,2006 + β5 𝑀𝑖,𝑡 𝑇𝑖,𝑡
+β6 𝑀𝑖,𝑡 𝐷𝑖,2006 + β7 𝑇𝑖,𝑡 𝐷𝑖,2006 + β8 𝑀𝑖,𝑡 𝑇𝑖,𝑡 𝐷𝑖,2006 + 𝑒𝑖,𝑡
(7)
Equation 7 uses a triple-difference (DiDiD) estimator to identify unskilled labor less than
25 years from 2006. The DiDiD estimator is β8. The regression will show the difference
if job opportunities were significantly different between years as a result of ending
conscription. If the DiDiD estimator is negative, it means unskilled labor has a worse
chance of employment after conscription ended, as predicted.
Some concern could be expressed over whether or not the DiDiD estimator is
actually identifying the effects of ending conscription or if it includes the effects of
immigration changes as well. Though it is impossible to separate conscripts from
immigrants in the labor market, it should be noted that net migration was constant in each
year observed (OECD, 2008). Therefore, it is reasonable to assume that the tripledifference estimator is not also identifying the effects of increased immigration in the
labor markets. Specifically, a decrease in employment opportunities among young
unskilled labor is expected to be the result of ending conscription, not an increase in
immigration.
29
Descriptive Statistics
The cross-sectional data are supplied by the Survey on Households’ Income and
Wealth for 2002 and 2006, undertaken bi-annually and selected at random by the Banca
D’Italia (see Brandolini and Cannari, 1994, for an extensive review of the methodology
and limitations). The data collected in 2002 includes 13,844 individuals while the data
collected in 2006 included 12,461 individuals. In Table 1, five different household
characteristic groups that are believed to influence the probability of being employed are
presented.
The first group contains information about age, sex, and employment status.
Women are only slightly more represented than men in each year. Data on skill level are
also presented, showing over 60% of the sample to be unskilled labor. Despite being
large in number, unskilled labor accounts for less than half of the employed force, while
almost all skilled individuals are employed. About 57% of the sample is observed as
employed in either year, which is expected as Italy has among the lowest employment
levels of OECD countries (OECD, 2010). Due to Italy’s burgeoning black market, labor
in this market could account for upwards of 20% of unrecorded employment. This
distinction should be addressed in future research to possibly include young males
working in the black market. This study, however, will only address formal job market
opportunities.
The second set of variables contains educational attainment data and lists the
highest level of education completed. Majority of the sample lists high school as the
30
highest level completed, with middle and elementary school in second and third,
respectively in 2002, but more middle school graduates in 2006. There are also more
people with bachelor’s degrees, indicating the treatment year is better educated. In the
third set of variables, over half the sample are married, with single people comprising
about 32% in both years. Widows and divorcees comprise the remaining 7 percent. The
fourth group contains regional variables that are fairly representative of the country,
accounting for the lower populations on the islands of Sicily and Sardinia. Most
observations are from the northeast (cities of Torino, Milan) and the south (Napoli, Bari).
Finally, data is available on the current sectors of work for employed people. Table 1
shows that a majority of workers are employed in mining and manufacturing and
government sectors. The service sector also employs a large and steady share of workers,
but the largest shifts in sectors came from domestic services, which grew over 1%
between sample years. Further descriptive statistics on youth aged between 18 and 24
years are presented in Table 1-B of Appendix B.
Table 1: Descriptive Statistics of Sample from 2002 and 2006, Individual Years
2002
2006
Observations
% of
Min Max Observations
% of
(n=13,844)
Sample
(n=12,461)
Sample
AGE, SEX, EMPLOYMENT, AND CONSCRIPTION STATUS
7390
53.81
0
1
7079
56.81
Employed
Unskilled labor
Of sample
Of employed
persons
Skilled labor
Of sample
Of employed persons
Male
Min
Max
0
1
9086
3380
65.44
24.41
0
0
1
1
7842
3196
62.93
25.64
0
0
1
1
4758
4010
6816
34.36
28.97
49.23
0
0
0
1
1
1
4619
3883
6149
37.07
31.16
49.35
0
0
0
1
1
1
31
Table 1 Continued
13844
42.25†
18
65
12461
23
.0017
0
1
HIGHEST LEVEL OF EDUCATION ATTAINED
No formal schooling
278
2.01
0
1
118
Elementary school
2424
17.51
0
1
1537
Middle school
4432
32.01
0
1
4170
Vocational school
931
6.72
0
1
984
High school
4533
32.74
0
1
4199
AA degree
103
0.74
0
1
141
BA/BS degree
1120
8.09
0
1
1277
Graduate degree
23
0.17
0
1
35
MARITAL STATUS
Married
8603
62.14
0
1
7607
Single
4450
32.14
0
1
3983
Separated/ Divorced
447
3.23
0
1
573
Widow(er)
344
2.48
0
1
298
REGRESSION OF RESIDENCE
Northeast
3359
24.26
0
1
2895
Northwest
2647
19.12
0
1
2689
Central
2880
20.80
0
1
2421
South
3179
22.96
0
1
2946
Islands
1779
12.85
0
1
1510
SECTORS OF EMPLOYMENT OF CURRENT WORKERS
Agriculture
400
5.41
0
1
326
Mining &
1926
26.06
0
1
1799
Manufacturing
Construction
542
7.33
0
1
565
Retail & Services
1191
16.12
0
1
1125
Transport
319
4.32
0
1
290
Finance
275
3.72
0
1
261
Real Estate
460
6.22
0
1
448
Domestic Services
356
4.82
0
1
428
Government
1894
25.63
0
1
1816
Foreign entities
27
0.37
0
1
21
Total Employed
7390
100
7079
†
Numbers presented are averages, not percent of sample
Age
Conscripted men
42.08†
18
65
0.95
12.33
33.46
7.90
33.70
1.13
10.25
0.28
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
61.05
31.96
4.60
2.39
0
0
0
0
1
1
1
1
23.23
21.58
19.43
23.64
12.12
0
0
0
0
0
1
1
1
1
1
4.61
25.41
0
0
1
1
7.98
15.89
4.10
3.69
6.33
6.05
25.65
0.30
100
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
Regression Strategy and Sign Predictions
To avoid perfect multicollinearity, the variables middle school, married, central, and
mining & manufacturing will be dropped from the household characteristics. The logit
32
model was preferred to the probit or linear specification using BIC and AIC testing
parameters. The results were similar between the logit and probit models, but the BIC
and AIC both showed a better fit with the logit model. In Equation 9, the logit regression
model for the treatment data is shown below:
𝑃
ln (
) = β0 + β1Xi + β2𝑈𝑛𝑠𝑘𝑖𝑙𝑙𝑒𝑑i + β3𝑈𝑛𝑑𝑒𝑟 25i +
(1 − 𝑒 𝐸𝑖,2006 )
β4𝑈𝑛𝑠𝑘𝑖𝑙𝑙𝑒𝑑i ∗ 𝑈𝑛𝑑𝑒𝑟 25i + εi
(9)
𝑃
ln (
) = β0 + β1𝑋i + β2𝑈𝑛𝑠𝑘𝑖𝑙𝑙𝑒𝑑i + β3𝑈𝑛𝑑𝑒𝑟 25i + β4𝑌𝑒𝑎𝑟 2006
(1 − 𝑒 𝐸𝑖,2002 )
+ β5𝑈𝑛𝑠𝑘𝑖𝑙𝑙𝑒𝑑i ∗ 𝑌𝑒𝑎𝑟 2006 + β6𝑈𝑛𝑠𝑘𝑖𝑙𝑙𝑒𝑑i ∗ 𝑈𝑛𝑑𝑒𝑟 25i
+ β7𝑈𝑛𝑑𝑒𝑟 25i ∗ 𝑌𝑒𝑎𝑟 2006
+ β8𝑈𝑛𝑑𝑒𝑟 25i ∗ 𝑌𝑒𝑎𝑟 2006 ∗ 𝑈𝑛𝑠𝑘𝑖𝑙𝑙𝑒𝑑i + εi
(10)
In Equation 10, the pooled data is regressed with a logit specification as well, but
interaction terms have been created to identify individuals from the 2006 sample. The
DiDiD estimator is believed to be negative, implying it was less probable for unskilled
labor to be employed after the reform. The significance of this statistic will be tested
using a standard z-score.
As for the household characteristics, age is expected to be negative initially, and
then positive as a person enters the middle of their life. Initially, a person will have
33
limited training and experience, making job opportunities scarce and good job matches
even scarcer. By middle age a person has acquired numerous skills and experience so
that finding employment should be easier than when they were younger. Being male is
expected to have positive and significant coefficients, though probably less so when
compared to previous decades as more women entered the workforce (Contini et al.,
2008). Educational attainment will have a positive influence on employment
opportunities as more education is consumed. All marital statuses are expected to have
positive coefficients, relative to being married except for single people – who may have
trouble finding employment while they are young, and widows – likely to be older and
retired.
Regionally, the northwest and northeast areas should be positive, as they are
known for their industry and growth (Rodriguez-Pose and Tselios, 2009; Nelson, 2006).
The agricultural south and the islands of Sicily and Sardinia, conversely, will have
negative coefficients, having been plagued for decades by chronic unemployment and
illegal immigration. Finally, most sectors of employment should have positive
coefficients, except for mining and manufacturing, which has been suffering among
industrialized countries since 1970 (“Industrial Metamorphosis,” 2005). Since mining
and manufacturing is dropped to avoid multicollinearity, all other job sectors are
expected to have positive coefficients.
34
Chapter 4
REGRESSION ANALYSIS
The results of the logit regressions for each year are given in Table 2. In both years, age
and age2 are highly statistically significant. The coefficient on age is positive, so job
opportunities increase with age, but at a decreasing rate. Young employees are
considered to be under the age of 35, the year for which employment opportunities begin
to decrease. In 2002, the probability of employment increased with more schooling,
except for bachelor’s degrees. Stated previously, Ballarino and Bratti (2009) found that
college graduates faced decreasing employment opportunities; the same effect is found
here, though somewhat counterintuitive as college is generally believed to increase
employment opportunities. In 2006, similar results were found with a notable exception
to graduate degrees, which did not significantly affect employment. In 2002, having a
graduate degree contributed to increasing the probability of being employed by 25%,
while being insignificant in 2006. Being single was negative and significant in both
years, indicating singles suffer the worst job prospects, as predicted9.
Regression results for the regional variables show the coefficients on northwest
are positive and significant as expected from the literature (Naticchioni et al., 2006).
People living in the south of Italy have a lower chance of employment when compared to
their northern counterparts in both years. The common explanation for this phenomenon
is that the north is more industrialized, and therefore has better job opportunities when
compared to the far more agrarian south. Peng and Siebert (2008) propose another
35
explanation, finding wages in Northern Italy to be procyclical with business cycles, in
contrast to Central and Southern Italy. This would imply that wages are determined for
northern employment conditions and sent south through centralized and coordinated
wage-setting institutions (national and sectoral unions). This causes wages outside
Northern Italy to respond to wage-agreements and not to local employment conditions.
As a result, wages are higher than equilibrium, leading to a permanent increase in
unemployment.
The coefficients on the work sectors indicate the best and worst sectors for
employment opportunities. Another way, they indicate the sectors where a willing
employee will have the estimated shortest and longest unemployment spells before
employment. All coefficients are positive, indicating that all listed sectors provide better
Table 2: Regression Results, Individual Years
Age
Age2
Male
No formal schooling
Elementary
Vocational school
High school
AA degree
BA/BS degree
Graduate degree
Single
Separated/ Divorced
Widow(er)
Northeast
Northwest
South
Islands
∂y/∂x
0.15732***
-0.00208***
0.37162***
-0.15381**
-0.02080**
0.08800**
0.09175**
-0.04357
-0.06838**
0.24955***
-0.07309***
0.08354**
0.11264***
0.02604
0.03954**
-0.22039***
-0.22045***
2002
(n=13,844)
S.E.
0.0057
0.0001
0.0129
0.0656
0.0220
0.0247
0.0165
0.0753
0.0268
0.0673
0.0203
0.0377
0.0439
0.0185
0.0193
0.0186
0.0216
P>z
0.000
0.000
0.000
0.019
0.344
0.000
0.000
0.563
0.011
0.000
0.000
0.027
0.010
0.159
0.041
0.000
0.000
∂y/∂x
0.14192***
-0.00185***
0.34169***
-0.26697***
-0.05180**
0.00935
0.05492***
-0.09322
-0.09119***
0.05134
-0.07001***
0.10050***
-0.04884
0.01573
0.10217***
-0.19324***
-0.18940***
2006
(n=12,461)
S.E.
0.0055
0.0001
0.0129
0.0843
0.0249
0.0246
0.0163
0.0634
0.0253
0.1139
0.0202
0.0313
0.0507
0.0189
0.0179
0.0201
0.0238
P>z
0.000
0.000
0.000
0.002
0.037
0.704
0.001
0.142
0.000
0.652
0.001
0.001
0.336
0.405
0.000
0.000
0.000
36
Table 2 Continued
Agriculture
Construction
Retail & Services
Real estate
Government
Foreign entities
Finance
Domestic services
Transportation
Unskilled labor
Under 25 years of age
Unskilled*(Age<25)
0.39993***
0.36943***
0.45319***
0.41387***
0.37671***
0.32975***
0.35222***
0.42355***
0.32520***
-0.42137***
0.25376***
-0.22214***
Pseudo R2
Log likelihood
.5025
-4758.60
0.0100
0.0125
0.0083
0.0103
0.0129
0.0782
0.0174
0.0082
0.0165
0.0143
0.0247
0.0316
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.34746***
0.33630***
0.39408***
0.32122***
0.35830***
0.20561*
0.27642***
0.37369***
0.29720***
-0.37532***
0.15086***
-0.12710***
0.0103
0.0116
0.0085
0.0146
0.0120
0.1092
0.0191
0.0083
0.0150
0.0148
0.0291
0.0360
0.000
0.000
0.000
0.000
0.000
0.060
0.000
0.000
0.000
0.000
0.000
0.000
.4770
-4456.78
*** = 1% significance level
** = 5% significance level
* = 10% significance level
employment opportunities than the omitted group, manufacturing and mining. In 2002,
the best sectors for work were retail and domestic services, while the worst, after mining
and manufacturing was transportation, as expected (Contini et al., 2008). In 2006, retail
and domestic service sectors again had the best opportunities for employment, while
construction and finance provided the worst job opportunities, after mining and
manufacturing. Finance seems out of place as the sector with the worst job opportunities
given the exponential rise in global financial networks in the last decade. Finance
markets closely tied to housing markets, and the global financial crisis may have hit this
sector particularly hard, though not officially beginning for another two years. It is
possible that because the finance sector only comprises 3.5% of employed people in
37
either year, the financial sector was still small and perhaps few job vacancies were
posted.
Men were predicted to have at least a 30% better chance of employment when
compared to women in both years. Since conscription concerns unskilled labor under the
age of twenty-five, interacted in the regression are the terms unskilled and under 25 years
of age to identify this group. Though both negative, one finds that unskilled labor in
2006 actually had better job opportunities when compared to unskilled labor in 2002, a
difference of 9.5%. This is contrary to the original theory which stated that job
opportunities would be worse as more unskilled labor under twenty-five entered the labor
force. As the young unskilled labor force increases, the probability of an individual
finding employment decreases as the labor market tightens. However, new labor
participants would be expected to be the cheapest labor around, but collective wage
agreements prevent businesses from taking advantage of the wage premium. As a result,
businesses are unable to expand and the new labor is forced into unemployment or the
unregulated black labor market.
To determine if the 9.5% difference in employment opportunities is statistically
different from zero, a DiDiD estimator is created for the pooled data. The results from
the logit regression are presented in Table 3. The DiDiD estimator was created by
interacting the terms unskilled, year 2006, and under 25 years of age to identify young
unskilled labor from the 2006 sample. Table 3 shows the DiDiD estimator to be
statistically insignificant, meaning that employment opportunities for unskilled labor
38
under twenty-five in 2006 were similar for unskilled labor of the same cohort four years
earlier. Stated in a broader way, when conscription ended in 2005, the 9.5% difference in
employment opportunities was not significantly different from labor conditions that
already existed in Italy between 2002 and 2006, and therefore probably not caused only
from conscription.
There are several reasons the DiDiD estimator may be insignificant. First, firms
could be operating in the long-run, having already absorbed the extra labor in the market,
and adjusting wages down in the new short-run. This does not seem to be a likely
explanation given that Italy’s wages are determined according to the 24-month inflation
prediction from the Banca D’Italia (Contini et al. 2008). Italy also has one of the most
rigid wage structures in Europe, meaning that wages are least likely to fall in Italy when
compared to other European countries. Even though wages were adjusted in May 2005
under a new collective bargaining agreement, they had a very small chance of being
reduced to reflect the increases in labor supply caused from conscription.
Upon further investigation, using data from the Survey of Household Income and
Wealth 2004, it was shown that during the last year of conscription, 19 men from the
Table 3: Regression Results, Pooled Data
Pooled Data
(n=26,305)
∂y/∂x
Age
Age2
Male
No formal schooling
0.14805***
-0.00194***
0.34393***
-0.19403***
Standard Error
P>z
0.0039
0.0001
0.0089
0.0512
0.000
0.000
0.000
0.000
39
Table 3 Continued
Elementary school
Vocational school
High school
AA degree
BA/BS degree
Graduate degree
Single
Separated/ Divorced
Widow(er)
Northeast
Northwest
South
Islands
Agriculture
Construction
Retail & Services
Real estate
Government
Foreign entities
Transportation
Domestic services
Finance
Unskilled labor
Under 25 years of age
Year 2006
Unskilled*Year 2006
Unskilled*(Age<25)
Year 2006*(Age<25)
Conscription effect
(Unskilled*Year
2006*Age<25)
-0.03951**
0.04829***
0.07210***
-0.07289
-0.07720***
0.12605
-0.07108***
0.09402***
0.02642
0.02167
0.07292***
-0.20602***
-0.20608***
0.37592***
0.35588***
0.42778***
0.36919***
0.37149***
0.27205***
0.31349***
0.40090***
0.31604***
-0.39038***
0.37195***
0.02315
0.00961
-0.38283***
-0.02380
-0.05703
Pseudo R2
Log likelihood
.4893
-9244.63
0.0162
0.0175
0.0117
0.0486
0.0184
0.0791
0.0142
0.0243
0.0339
0.0133
0.0133
0.0136
0.0160
0.0072
0.0085
0.0060
0.0091
0.0088
0.0681
0.0111
0.0059
0.0132
0.0128
0.0336
0.0180
0.0210
0.0637
0.0368
0.0392
0.015
0.006
0.000
0.134
0.000
0.111
0.000
0.000
0.436
0.102
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.199
0.648
0.000
0.518
0.146
*** = 1% significance level
** = 5% significance level
* = 10% significance level
sample were currently in service as conscripts. Rough calculations based on the sample
place the number of conscripts at 53,706 men, while the government quota system,
written in 2000, predicted around 40,000 conscripts would be necessary. Using either
40
estimate, it would still place the number of conscripts entering the labor force anywhere
from one-fifth to one-tenth of the number of immigrants entering the country each year.10
The collective bargaining agreements of 2001 and 2004 make no mention of the new
unconscripted labor force, quite possibly because it is of such a small concern.
Immigration, however, was an issue of large enough scale to warrant inclusion in the
discussion of collective bargaining rights (“New Legislation,” 2002).
Immigration is a major concern for Italy, as it is a destination country for many,
including refugees, from Eastern Europe and Africa. In a recent study, Venturini and
Villosio (2008) found that immigrants make the same wage as native Italians and
experience similar job opportunities. They find the Italian labor market is no different
from several other industrialized countries, both in terms of employment for immigrants
and the market’s inability to sustain continuous employment for them. Italy does differ,
however, in the fact that it experiences one of the lowest emigration rates in Europe,
forcing more pressure on the labor market.
Immigrants are mostly found in manufacturing and service sectors, as expected.
This also implies a larger than average black labor market in those sectors, where
collective bargaining agreements are ineffective. And since native workers of the youth
labor pool are already competing with immigrants for jobs, it would necessarily follow
that their employment opportunities decrease even further given conscription ending.
This study assumed constant migration patterns and ignored the black labor market of
41
income earners since official employment statistics would consider them as unemployed
or outside the labor force.
A continuation of the low-skilled jobs theory requires that the black labor market
and immigration issues be examined as well. Stated earlier, collective bargaining
agreements and unions only cover about 80 percent of workers in the economy. The data
also listed many people as income earners without being listed as employed. Both of
these facts could be indicative of a high-functioning black labor market. When
traditional methods of employment are fruitless, it is not uncommon for people and
businesses to enter into labor contracts “under the table” whereby both parties avoid
reporting taxes. Naticchioni et al. (2006) report that black markets account for 11% of
the labor market in Northern and Central Italy, but over 22% in the Southern region.
With lower labor costs, unskilled labor can find work easier at prevailing market wages,
not higher collective bargaining agreements. Unskilled labor and conscripts from
Southern Italy are more likely to enter this market when compared to their counterparts in
other regions.
Finally, this study may be measuring the eligibility effect, and not the direct
effects of conscription. An earlier assumption required all men in the sample to have
been eligible for service. Of course not all men would pass the physical, medical, and
psychological examinations to enter the military, but this is believed to be a small number
of the total cohort of males (Di Pietro, 2009).
42
An issue of importance that Di Pietro (2009) neglected to account for was that of
the conscientious objector. For professional soldiers, conscientious objection is not
recognized and is punishable with up to five years in prison. On the other hand, the
Italian government recognized that military conscription was not for everybody, either
due to political or moral disagreements. Italy did offer an alternative service program
that required the same pay and time commitment of one year. Act n.230/1998 allowed
for civil service within or outside Italy to be counted as a person’s conscription service,
as the action was still defending the “core principles” of the Italian Constitution
(“Conscientious Objector,” 1998). Around 80,000 men of the possible conscripted force
chose alternative service each year (Piattelli, 2001). If a person still objected, a prison
sentence of six months to two years could be served instead.
Addressing the role of the conscientious objector is important to this study
because if civil service was chosen over military conscription, perhaps the civil service
workers were counted as employed. The employment, conscription, and sector of
employment observations would be misrepresented.
Heterogeneous Data Concerns
Di Pietro (2009) found that his data exhibited a heterogeneous effect between men of
eighteen- and nineteen-years old when examining their entrance into college. Though
eighteen-year old men were found to enter college at rates no different before and after
the conscription reform, he found a positive effect of conscription for the latter group.
43
He also found that when controlling for the education of the parents, ending conscription
was both better for students from advantaged backgrounds and detrimental for those from
disadvantaged backgrounds in terms of university attendance rates. Although male
individuals with higher discount rates chose lower levels of schooling, the presence of
conscription could have diverted them from a longer investment in schooling (Di Pietro,
2009). Stated differently, men from disadvantaged backgrounds might have chosen
school over work if conscription had not made the total investment time five years,
instead of the normal four years from university attendance.
To account for such concerns given, as this study was primarily concerned with
unskilled labor below the age of 25 and their employment opportunities, further
regressions of the pooled data were conducted to determine if heterogeneity existed in the
data (see Appendix B, Table 2-B). Looking at the DiDiD estimator for each age
individually from 18 to 24, the data revealed that there was indeed no effect on
employment opportunities caused by ending conscription, and therefore no heterogeneity
(see Appendix B, Table 3-B). However, based on the literature (Contini, 2002; Contini et
al., 2008), it would be advantageous to examine different cohorts between the two years
since there has been a shift in hiring by firms. As previously stated, the cohort of men
aged 25 to 30 years is now the primary hiring group for SMEs.
The two cohorts to be examined will be unskilled labor between the ages of 25
and 30 and those between the ages of 31 and 40. Descriptive statistics of both cohorts in
each year are presented in Tables 4 and 5. Workers in their late-twenties were employed
44
less often in 2006 when compared to 2002, but were more likely to be employed in
skilled positions. Members of this cohort were also less likely to graduate high school
and more likely to enter unskilled labor positions in the construction and manufacturing
sectors. Conscription is suspect here because lower employment could indicate jobs
being shifted to the younger, cheaper cohort, as theory predicts. Also, a choice of lower
education consumption indicates a higher discount rate and willingness to forgo college
and take unskilled positions in the sectors previously mentioned.
The next oldest cohort, men between 31 and 40 years, are better employed than
their counterparts of four years earlier. They have fewer high school graduates compared
to 2002, but over 3% more members of their cohort finished a university degree. As a
result, skilled labor outnumbers unskilled labor by almost a six point margin.
Conscription appears to be affecting the employment opportunities of this cohort, even
though they are better trained and probably not competing for similar positions.
Table 4: Descriptive Statistics, Cohorts Aged Between 25-30 Years, Individual Years
Pooled Data
Observed aged 25-30 Years
2002
2006
Observations
% of
Min Max Observations
% of
(n=1,646)
Sample
(n=1,330)
Sample
AGE, SEX, EMPLOYMENT, AND CONSCRIPTION STATUS
981
59.60
0
1
712
53.53
Employed
Unskilled labor
Of sample
Of employed persons
Skilled labor
Of sample
Of employed persons
Male
Age
Min
Max
0
1
1150
498
69.87
30.25
0
0
1
1
876
418
65.86
31.42
0
0
1
1
496
483
849
1646
30.13
29.34
51.58
27.45†
0
0
0
25
1
1
1
30
454
438
856
1330
34.13
32.93
64.36
27.44†
0
0
0
25
1
1
1
30
45
Table 4 Continued
Conscripted men
1
0.06
0
1
HIGHEST LEVEL OF EDUCATION ATTAINED
No formal schooling
10
0.61
0
1
3
Elementary school
54
3.28
0
1
16
Middle school
459
27.89
0
1
341
Vocational school
112
6.80
0
1
120
High school
766
46.54
0
1
544
AA degree
25
1.52
0
1
29
BA/BS degree
217
13.18
0
1
274
Graduate degree
3
0.18
0
1
3
MARITAL STATUS
Married
361
21.93
0
1
276
Single
1271
77.22
0
1
1037
Separated/ Divorced
13
0.79
0
1
17
Widow(er)
1
0.06
0
1
0
REGION OF RESIDENCE
Northeast
332
20.17
0
1
261
Northwest
337
20.47
0
1
284
Central
325
19.74
0
1
265
South
430
26.12
0
1
334
Islands
222
13.49
0
1
186
SECTORS OF EMPLOYMENT OF CURRENT WORKERS
Agriculture
48
4.89
0
1
42
Mining &
310
31.60
0
1
253
Manufacturing
Construction
81
8.26
0
1
83
Retail & Services
169
17.23
0
1
162
Transport
58
5.91
0
1
39
Finance
28
2.85
0
1
35
Real Estate
85
8.66
0
1
66
Domestic Services
48
4.89
0
1
44
Government
148
15.09
0
1
130
Foreign entities
6
0.61
0
1
2
Total Employed
981
100
712
†
Numbers presented are averages, not percent of sample
0.23
1.20
25.64
9.02
40.90
2.18
20.60
0.23
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
20.75
77.97
1.28
0
0
0
0
0
1
1
1
1
19.62
21.35
19.92
25.11
13.98
0
0
0
0
0
1
1
1
1
1
5.90
35.53
0
0
1
1
11.66
22.75
5.48
4.92
9.27
6.18
18.26
0.28
100
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
46
Table 5: Descriptive Statistics, Cohorts Aged Between 31-40 Years, Individual Years
Pooled Data
Observed aged 31-40 Years
2002
2006
Observations
% of
Min Max Observations
% of
(n=2,792)
Sample
(n=2,474)
Sample
AGE, SEX, EMPLOYMENT, AND CONSCRIPTION STATUS
2019
72.31
0
1
1852
74.86
Min
Max
0
1
58.04
34.47
0
0
1
1
1129
40.44
0
1
1038
41.96
1108
39.69
0
1
999
40.38
1341
48.03
0
1
1210
48.91
2792
35.66†
31
40
2474
35.76†
1
0.04
0
1
HIGHEST LEVEL OF EDUCATION ATTAINED
No formal schooling
25
0.90
0
1
13
0.53
Elementary school
138
4.94
0
1
82
3.31
Middle school
1086
38.90
0
1
870
35.17
Vocational school
224
8.02
0
1
217
8.77
High school
980
35.10
0
1
903
36.50
AA degree
33
1.18
0
1
35
1.41
BA/BS degree
300
10.74
0
1
344
13.90
Graduate degree
6
0.21
0
1
10
0.40
MARITAL STATUS
Married
1785
63.93
0
1
1522
61.52
Single
888
31.81
0
1
834
33.71
Separated/ Divorced
109
3.90
0
1
111
4.49
Widow(er)
10
0.36
0
1
7
0.28
REGION OF RESIDENCE
Northeast
664
23.78
0
1
544
21.99
Northwest
581
20.81
0
1
608
24.58
Central
552
19.77
0
1
460
18.59
South
635
22.74
0
1
596
24.09
Islands
360
12.89
0
1
266
10.75
SECTORS OF EMPLOYMENT OF CURRENT WORKERS
Agriculture
97
4.80
0
1
72
3.89
Mining &
551
27.29
0
1
517
27.92
Manufacturing
Construction
152
7.53
0
1
152
8.21
0
0
0
31
1
1
1
40
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
0
1
1
1
1
1
0
1
0
1
0
1
Employed
Unskilled labor
Of sample
Of employed persons
Skilled labor
Of sample
Of employed persons
Male
Age
Conscripted men
1663
911
59.56
32.63
0
0
1
1
1436
853
47
Table 5 Continued
Retail & Services
327
16.20
0
Transport
87
4.31
0
Finance
73
3.62
0
Real Estate
160
7.92
0
Domestic Services
109
5.40
0
Government
449
22.24
0
Foreign entities
14
0.69
0
Total Employed
2019
100
†
Numbers presented are averages, not percent of sample
1
1
1
1
1
1
1
302
66
72
142
121
405
3
1852
16.31
3.56
3.89
7.67
6.53
21.87
0.16
100
0
0
0
0
0
0
0
1
1
1
1
1
1
1
In Table 6, the pooled regression results are presented for both cohorts. It can be
seen that after conscription, only unskilled labor of the oldest cohort experienced
significant employment opportunity changes caused by the conscription reform. Stated
another way, employment opportunities for unskilled labor, 31-40 years, increased by
19.5% after conscription ended, relative to unskilled labor of different cohorts and skilled
labor of 31-40 years. This observation is to be expected, though not initially clear.
Skilled labor most likely completed college, where job opportunities had been declining
for several years. This makes unskilled labor more attractive. Second, since the data
reach their maximum probability of being employed at age 34, this group would be the
most likely to remain employed. In times of neither job destruction nor job creation,
firms would shift their work force to get the best workers for the cheapest price. Given
the stagnant economy of Italy in the past decade (World Bank, 2011), this is a genuine
possibility. Though positive and statistically significant at the one percent level, it is hard
to accept that ending conscription increased job opportunities for older unskilled labor by
48
19.5%. The oldest cohort examined may have positive coefficients due to 2006 being the
highest production year Italy experienced in the past decade (World Bank, 2011).
Members of cohorts older than forty have notably not been the focus of the
analysis given that the job market between career professionals and young unskilled labor
could not be further apart. Newly unconscripted men are not competing for positions of
executives. If executives are being pushed out in favor of younger, cheaper people, the
cohort being affected would certainly not involve anyone under the age of twenty-five.
Table 6: Regression Results, Pooled Cohorts
Age
Age2
Male
No formal schooling
Elementary school
Vocational school
High school
AA degree
BA/BS degree
Graduate degree
Single
Separated/ Divorced
Widow(er)
Northeast
Northwest
South
Islands
Agriculture
Construction
Retail & Services
Real estate
Government
Foreign entities
Transport
Pooled Data
Observed aged 25-30 Years
(n=13,844)
∂y/∂x
S.E.
P>z
0.13423***
0.0029
0.000
-0.00179***
0.0000
0.000
0.34202***
0.0087
0.000
-0.19592***
0.0508
0.000
-0.04358***
0.0161
0.007
0.04918***
0.0174
0.005
0.06843***
0.0116
0.000
-0.08335
0.0509
0.101
-0.09653***
0.0186
0.000
0.12231
0.0796
0.124
-0.07632***
0.0144
0.000
0.09498***
0.0238
0.000
0.02461
0.0333
0.460
0.02264*
0.0132
0.086
0.07095***
0.0133
0.000
-0.20670***
0.0137
0.000
-0.20262***
0.0160
0.000
0.37121***
0.0072
0.000
0.35164***
0.0084
0.000
0.42397***
0.0060
0.000
0.36464***
0.0091
0.000
0.37128***
0.0087
0.000
0.25566***
0.0757
0.001
0.30903***
0.0112
0.000
Pooled Data
Observed aged 31-40 Years
(n=12,461)
∂y/∂x
S.E.
P>z
0.14381***
0.003
0.000
-0.00193***
0.000
0.000
0.33762***
0.011
0.000
-0.19001***
0.050
0.000
-0.04877***
0.016
0.002
0.04346**
0.017
0.012
-0.07888***
0.012
0.000
-0.09053*
0.050
0.068
-0.10239***
0.019
0.000
0.13082*
0.076
0.084
-0.08706***
0.014
0.000
0.09233***
0.024
0.000
0.02504
0.034
0.458
0.02216*
0.013
0.091
0.07319***
0.013
0.000
-0.20650***
0.014
0.000
-0.20674***
0.016
0.000
0.36812***
0.007
0.000
0.34800***
0.009
0.000
0.42030***
0.006
0.000
0.36272***
0.009
0.000
0.36576***
0.009
0.000
0.25690***
0.073
0.000
0.30795***
0.011
0.000
49
Table 6 Continued
Domestic services
Finance
Unskilled labor
Year 2006
Unskilled*Year 2006
Unskilled*(25≤Age≤30)
Between 25-30 years of
age
Year
2006*(25≤Age≤30)
Conscription effect
(25≤Age≤30)
Unskilled*(31≤
Age≤40)
Between 31–40 years of
age
Year 2006*(31≤ Age
≤40)
Conscription effect
(31≤ Age ≤40)
0.39697***
0.31571***
-0.37066***
0.02597
-0.00258
-0.40581***
0.30796***
0.0059
0.0128
0.0132
0.0182
0.0213
0.0595
0.0406
0.000
0.000
0.000
0.155
0.903
0.000
0.000
-0.10075
0.1086
0.353
0.09031
0.0964
0.349
Pseudo R2
Log likelihood
.4891
-9248.79
0.39310***
0.31550***
-0.37481***
0.02361*
-0.02082
0.006
0.013
0.011
0.013
0.0225
0.000
0.000
0.000
0.060
0.355
-0.36072***
0.052
0.000
0.17227***
0.048
0.000
-0.22465***
0.072
0.002
0.19495***
0.052
0.000
.4920
-9195.63
*** = 1% significance level
** = 5% significance level
* = 10% significance level
50
Chapter 5
STUDY LIMITATIONS AND CONCLUSION
Conscription as a subject conjures up different thoughts in people, ranging from the
necessary and patriotic to the barbaric and archaic. It has far reaching implications for
millions of youth worldwide. It will impact university choices, family planning
decisions, and labor markets. This study was devoted to the latter. Since 1997, countries
in Europe have been ending the practice of conscription for various reasons. One
possible cause is membership in NATO, another claims it is the result of the toppling of
the Soviet Union. Despite these theories, a constant in all countries is the impact on the
unskilled labor market. As the military pays higher wages for professional soldiers, some
youth will still enlist, but others will go into the private sector. Examined previously was
the effect of conscription on employment opportunities after Italy ended conscription in
2005.
The theoretical and empirical frameworks were adopted from Yashiv (2000) –
construction of a partial-equilibrium labor market – and Di Pietro (2009) – the DiDiD
regression strategy, respectively. The theoretical model in Section 3 first constructed a
matching function, where the flow of new hires was influenced by technology, search
intensity, and job vacancies available. Equation 2 stated a change in unemployment was
influenced by the matching function, job destruction, and labor supply. Holding the
matching function constant, an increase in the labor supply next period increased
unemployment in the next period as well. This shift can visibly be seen in Figure 1. This
51
means that employment opportunities for workers should have decreased following the
end of conscription.
The Survey on Household Income and Wealth 2002 and 2006 provided household
characteristics for a representative sample of Italy, around 13,000 individuals observed
each year. Initial analysis of individual years revealed than unskilled workers in 2006
had a 9.5% better chance of being employed when compared to their cohort of four years
earlier. To determine if this difference was significant, a triple-difference estimator was
constructed after the data were pooled. This approach is preferred as it is the only model
available that attempts to measure conscription effects between years (Imbens and Van
Der Klaauw, 1995; Card and Lemieux, 2001; Maurin and Xenogiani, 2005; Di Pietro,
2009).
Regression results of the logit model, presented in Table 3, show that employment
opportunities for young unskilled workers were not significantly impacted by the end of
conscription. The effect was believed to be too small, specifically when compared to
immigration numbers, which were six times higher than conscripts in 2003. The positive
outlook for unskilled labor may have been caused from the GDP growth of 3%, the
highest of the past decade, in 2006. Further analysis was conducted to check for
heterogeneity among individual years in the youngest cohort, and impacts on older
cohorts. For unskilled labor between the ages of 31 and 40, the DiDiD estimator was
positive and significant, but all others showed no measurable impact on labor market
52
conditions. This leads the belief that ending conscription had no negative impacts on
employment opportunities, contrary to the original theory.
Two assumptions were required to analyze the effect of conscription of Italy.
First, all men were considered to have been eligible and indeed served as a conscript.
Notably omitted were ineligible men and conscientious objectors. Di Pietro (2009) did
not believe ineligible men were of a great number, but conscientious objects numbered
near one-third of the conscriptable males each year. Assumption two required that no
shocks other than conscription affected the labor markets for skilled and unskilled labor
differently. This was perhaps unreasonable given the Bossi-Fini immigration reform of
2002 and the existence of the black labor market. Young unskilled workers would most
likely be competing with immigrants for similar positions, which may or may not be in
the legal labor market. If not, then this study could suffer from omitted variable bias by
making an unrealistic assumption.
This study primarily addressed the short-run impact of ending conscription in
Italy. If a long-run equilibrium were to be sought, one could no longer ignore
immigration flows, given the recent influx of migrants and refugees from Egypt and
Tunisia. Tunisia was already a major supplier of illegal immigrants to Italy prior to the
Jasmine Revolution’s end in January. An increase has already been seen in Tunisians
seeking work and asylum, but more could be expected from Egypt following their
revolution and possibly Libya or Algeria if their governments fall in the coming weeks.
53
Even though the labor markets did not appear to be significantly affected by
conscription, ending this rite of passage will certainly have a cultural impact. Men with
high discount rates may choose college or starting a family earlier in life since they have
effectively been given one year of their life back. Studies have already been undertaken
in a few countries (Italy, Spain, and France) on the effect of conscription and education
consumption, but future studies should evaluate the impact of how family planning
dynamics have changed among young men. Family planning decisions will play an
important role in labor skill decisions later. Outside Italy, similar studies could be
conducted to measure employment opportunities. In a country like Germany, with its
high immigration rate and powerhouse economy, similar results might be expected.
However, there may be interesting effects from ending conscription in smaller countries,
such as Albania or Ukraine. Developing countries are likely to have a greater need for
unskilled labor, whereby ending conscription could drive production growth.
Furthermore, one could examine the collective impact in Europe after twenty countries
end conscription between the years of 1997 and 2012.
54
END NOTES
1
This would change later as more wealth was created and more poor disposable youth
were being born.
2
This may not be true in Italy as police officers, firemen, and carabinieri (Italian
gendarmerie) are required to complete at least one year of military service before entering
the training program.
3
I believe this given that after conscription is abolished, the labor has to go somewhere.
The only options remaining are a university education or the labor force, treating the
military like another sector of employment; it must pay a competitive wage. Di Pietro
(2009) found no change in university enrollment in Italy, though it was observed in
France (Maurin & Xenogiani, 2005).
4
Naturally, economics and other “quantitative” disciplines had the best outcomes.
5
Author’s calculations using number of crude births and number of conscripts per year.
6
The issue of heterogeneity will be addressed in Section 4.2.
7
A likely effect given the bargaining power of workers.
55
8
Even though the financial crisis would affect all genders and ages, it would be more
telling of actual conditions to examine employment opportunities outside a crisis that has
caused such high unemployment already. Also, the longer the time intervals between
observation dates will give the labor markets more time to recover from the supply shock,
making it harder to detect the effect of conscription.
9
It is still believed in Italy (and several post-Soviet countries) that the best way for
gaining meaningful employment was to wait for a parent (usually the father) to pass on
and move into their position. Though there is no empirical evidence of this rampant
paternal nepotism, it is important to note as it could be a cause for discouraged workers to
quit looking for employment and morbidly wait around.
10
In 2002 and 2003, Italy experienced an increase of 3.9% and 4.2% in net migration,
respectively. This placed Italy in the top three countries (Spain, Ireland) with the highest
net migration rates in the OECD countries, over twice the average (OECD, 2007).
56
APPENDIX A
Calculations of the Theoretical Model
Seen in Yashiv (2000), the partial-equilibrium unskilled labor market can be solved in
terms of 𝑈1,𝑡+1
𝑈1,𝑡+1 − 𝑈1,𝑡 = −M(𝜇1,𝑡 , 𝐶1,𝑡 𝑈1,𝑡 , 𝑉1,𝑡 ) + 𝑠1,𝑡+1 (𝐿1,𝑡 − 𝑈1,𝑡 ) + (𝐿1,𝑡+1 − 𝐿1,𝑡 )
𝑈1,𝑡+1 = −M(𝜇1,𝑡 , 𝐶1,𝑡 𝑈1,𝑡 , 𝑉1,𝑡 ) + 𝑠1,𝑡+1 (𝐿1,𝑡 − 𝑈1,𝑡 ) + (𝐿1,𝑡+1 − 𝐿1,𝑡 ) + 𝑈1,𝑡
𝑈1,𝑡+1 = −M(𝜇1,𝑡 , 𝐶1,𝑡 𝑈1,𝑡 , 𝑉1,𝑡 ) + 𝐿1,𝑡 (𝑠1,𝑡+1 − 1) + 𝑈1,𝑡 (𝑠1,𝑡+1 − 1) + 𝐿1,𝑡+1
(1-A)
Taking the partial derivative of Equation 1-A w.r.t. 𝐿1,𝑡+1 gives the following
𝜕𝑈1,𝑡+1
𝜕𝐿1,𝑡+1
=1
(2-A)
In Equation 2, it is shown that any increase in the unskilled labor supply will increase
unskilled unemployment by one unit. After the end of conscription, an increase in the
next period unskilled labor supply will necessarily cause an increase in the next period
unskilled unemployment rate. Conversely, Equations 3-A and 4-A show that an increase
in the unskilled labor supply will not affect skilled employment opportunities in the next
period.
𝑈2,𝑡+1 − 𝑈2,𝑡 = −M(𝜇2,𝑡 , 𝐶2,𝑡 𝑈2,𝑡 , 𝑉2,𝑡 ) + 𝑠2,𝑡+1 (𝐿2,𝑡 − 𝑈2,𝑡 ) + (𝐿2,𝑡+1 − 𝐿2,𝑡 )
𝑈2,𝑡+1 = −M(𝜇2,𝑡 , 𝐶2,𝑡 𝑈2,𝑡 , 𝑉2,𝑡 ) + 𝑠2,𝑡+1 (𝐿2,𝑡 − 𝑈2,𝑡 ) + (𝐿2,𝑡+1 − 𝐿2,𝑡 ) + 𝑈2,𝑡
𝑈2,𝑡+1 = −M(𝜇2,𝑡 , 𝐶2,𝑡 𝑈2,𝑡 , 𝑉2,𝑡 ) + 𝐿2,𝑡 (𝑠2,𝑡+1 − 1) + 𝑈2,𝑡 (𝑠2,𝑡+1 − 1) + 𝐿2,𝑡+1
(3-A)
57
Taking the partial derivative of Equation 3-A w.r.t. 𝐿1,𝑡+1 gives the following
𝜕𝑈2,𝑡+1
𝜕𝐿1,𝑡+1
=0
(4-A)
58
APPENDIX B
Further Regressions to Check for Heterogeneity
Table 1-B
Persons less than 25 years
Persons less than 25 years
in 2002
in 2006
Observations
% of
Min Max Observations
% of
Min Max
(n=1,777)
Sample
(n=1,493)
Sample
AGE, SEX, EMPLOYMENT, AND CONSCRIPTION STATUS
506
28.47
0
1
408
27.33
0
1
Employed
Unskilled labor
Of sample
Of employed persons
Skilled labor
Of sample
Of employed persons
Male
Age
Conscripted men
1610
343
90.60
19.30
0
0
1
1
1347
272
167
9.40
0
1
146
163
9.17
0
1
136
949
53.40
0
1
789
1777
21.06†
18
24
1493
19
1.07
0
1
HIGHEST LEVEL OF EDUCATION ATTAINED
No formal schooling
6
0.34
0
1
2
Elementary school
33
1.86
0
1
17
Middle school
598
33.65
0
1
471
Vocational school
119
6.70
0
1
101
High school
989
55.66
0
1
818
AA degree
7
0.39
0
1
28
BA/BS degree
25
1.41
0
1
55
Graduate degree
0
0
0
1
1
MARITAL STATUS
Married
42
2.36
0
1
55
Single
1732
97.47
0
1
1438
Separated/ Divorced
3
0.17
0
1
0
Widow(er)
0
0
0
1
0
REGION OF RESIDENCE
Northeast
366
20.60
0
1
299
Northwest
279
15.70
0
1
292
Central
378
21.27
0
1
270
South
486
27.35
0
1
425
Islands
268
15.08
0
1
207
SECTORS OF EMPLOYMENT OF CURRENT WORKERS
Agriculture
22
4.35
0
1
13
Mining &
198
39.13
0
1
144
Manufacturing
Construction
34
6.72
0
1
41
Retail & Services
119
23.52
0
1
108
Transport
12
2.37
0
1
18
90.22
18.22
0
0
1
1
9.80
9.11
52.85
21.04†
0
0
0
18
1
1
1
24
0.13
1.14
31.55
6.76
54.79
1.88
3.68
0.07
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
3.68
96.32
0
0
0
0
0
0
1
1
1
1
20.03
19.56
18.08
28.47
13.86
0
0
0
0
0
1
1
1
1
1
3.19
0
1
35.29
0
1
10.05
26.47
4.41
0
0
0
1
1
1
59
Table 1-B Continued
Finance
Real Estate
Domestic Services
Government
Foreign entities
Total Employed
†Numbers
20
32
27
42
0
506
3.95
6.32
5.34
8.30
0
100
0
0
0
0
0
1
1
1
1
1
7
28
24
25
0
408
1.72
6.86
5.88
6.13
0.00
100
0
0
0
0
0
presented are averages, not percent of sample
Table 2-B
Pooled Data
(n=26,305)
Conscription effects of men aged 18-24 years
∂y/∂x
Age
Age2
Male
No formal schooling
Elementary school
Vocational school
High school
AA degree
BA/BS degree
Graduate degree
Single
Separated/ Divorced
Widow(er)
Northeast
Northwest
South
Islands
Agriculture
Construction
Retail & Services
Real estate
Government
Foreign entities
Transportation
Domestic services
Finance
Unskilled labor
Year 2006
0.13416***
-0.00180***
0.33989***
-0.18915***
-0.03411**
0.04821***
0.07094***
-0.07410
-0.09207***
0.11770
-0.07176***
0.09502***
0.02390
0.02337*
0.07298***
-0.20738***
-0.20491***
0.37605***
0.35641***
0.42835***
0.36976***
0.37083***
0.26550***
0.31497***
0.40162***
0.31828***
-0.40147***
0.02180
Standard Error
P>z
0.0032
0.0000
0.0087
0.0514
0.0162
0.0176
0.0117
0.0494
0.0184
0.0810
0.0143
0.0243
0.0340
0.0132
0.0133
0.0136
0.0159
0.0072
0.0085
0.0059
0.0090
0.0088
0.0716
0.0110
0.0059
0.0129
0.0122
0.0179
0.000
0.000
0.000
0.000
0.035
0.006
0.000
0.133
0.000
0.146
0.000
0.000
0.482
0.077
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.222
1
1
1
1
1
60
Table 2-B Continued
Unskilled*Year 2006
Conscription effect
(Unskilled *Year 2006*Age 18)
Conscription effect
(Unskilled *Year 2006*Age 19)
Conscription effect
(Unskilled *Year 2006*Age 20)
Conscription effect
(Unskilled *Year 2006*Age 21)
Conscription effect
(Unskilled *Year 2006*Age 22)
Conscription effect
(Unskilled *Year 2006*Age 23)
Conscription effect
(Unskilled *Year 2006*Age 24)
0.01010
0.0210
0.630
-0.02300
0.0407
0.572
-0.03933
0.0436
0.367
0.04988
0.0343
0.146
-0.02094
0.0339
0.537
-0.04028
0.0384
0.294
-0.05274
0.0447
0.238
-0.06158
0.0416
0.139
Pseudo R2
Log likelihood
.4872
-9282.01
*** = 1% significance level
** = 5% significance level
* = 10% significance level
Table 3-B
Chi2 Test for Significance
18 year old conscript = 0
19 year old conscript = 0
20 year old conscript = 0
21 year old conscript = 0
22 year old conscript = 0
23 year old conscript = 0
24 year old conscript = 0
Chi2 (7) = 10.03
Prob > Chi2 = 0.1869
61
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